Predict Drug Resistance Research Articles

Computer-Aided Optimization of Combined Anti-Retroviral Therapy for HIV: New Drugs, New Drug Targets and Drug Resistance.

Resistance to antiretroviral drugs is a complex and evolving area with relevant implications in the treatment of human immunodeficiency virus (HIV) infection. Several rules, algorithms and full-fledged computer programs have been developed to assist the HIV specialist in the choice of the best patient-tailored therapy. Experts' rules and statistical/machine learning algorithms for interpreting HIV drug resistance, along with their program implementations, were retrieved from PubMed and other on-line resources to be critically reviewed in terms of technical approach, performance, usability, update, and evolution (i.e. inclusion of novel drugs or expansion to other viral agents). Several drug resistance prediction algorithms for the nucleotide/nucleoside/non-nucleoside reverse transcriptase, protease and integrase inhibitors as well as coreceptor antagonists are currently available, routinely used, and have been validated thoroughly in independent studies. Computer tools that combine single-drug genotypic/phenotypic resistance interpretation and optimize combination antiretroviral therapy have been also developed and implemented as web applications. Most of the systems have been updated timely to incorporate new drugs and few have recently been expanded to meet a similar need in the Hepatitis C area. Prototype systems aiming at predicting virological response from both virus and patient indicators have been recently developed but they are not yet being routinely used. Computing HIV genotype to predict drug susceptibility in vitro or response to combination antiretroviral therapy in vivo is a continuous and productive research field, translating into successful treatment decision support tools, an essential component of the management of HIV patients.

Lopinavir Resistance Classification with Imbalanced Data Using Probabilistic Neural Networks.

Resistance to antiretroviral drugs has been a major obstacle for long-lasting treatment of HIV-infected patients. The development of models to predict drug resistance is recognized as useful for helping the decision of the best therapy for each HIV+ individual. The aim of this study was to develop classifiers for predicting resistance to the HIV protease inhibitor lopinavir using a probabilistic neural network (PNN). The data were provided by the Molecular Virology Laboratory of the Health Sciences Center, Federal University of Rio de Janeiro (CCS-UFRJ/Brazil). Using bootstrap and stepwise techniques, ten features were selected by logistic regression (LR) to be used as inputs to the network. Bootstrap and cross-validation were used to define the smoothing parameter of the PNN networks. Four balanced models were designed and evaluated using a separate test set. The accuracies of the classifiers with the test set ranged from 0.89 to 0.94, and the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.96 to 0.97. The sensitivity ranged from 0.94 to 1.00, and the specificity was between 0.88 and 0.92. Four classifiers showed performances very close to three existing expert-based interpretation systems, the HIVdb, the Rega and the ANRS algorithms, and to a k-Nearest Neighbor.

Application of PK/PD Modeling in Veterinary Field: Dose Optimization and Drug Resistance Prediction

Among veterinary drugs, antibiotics are frequently used. The true mean of antibiotic treatment is to administer dose of drug that will have enough high possibility of attaining the preferred curative effect, with adequately low chance of concentration associated toxicity. Rising of antibacterial resistance and lack of novel antibiotic is a global crisis; therefore there is an urgent need to overcome this problem. Inappropriate antibiotic selection, group treatment, and suboptimal dosing are mostly responsible for the mentioned problem. One approach to minimizing the antibacterial resistance is to optimize the dosage regimen. PK/PD model is important realm to be used for that purpose from several years. PK/PD model describes the relationship between drug potency, microorganism exposed to drug, and the effect observed. Proper use of the most modern PK/PD modeling approaches in veterinary medicine can optimize the dosage for patient, which in turn reduce toxicity and reduce the emergence of resistance. The aim of this review is to look at the existing state and application of PK/PD in veterinary medicine based on in vitro, in vivo, healthy, and disease model.

Prediction of Cancer Drug Resistance and Implications for Personalized Medicine.

Drug resistance still impedes successful cancer chemotherapy. A major goal of early concepts in individualized therapy was to develop in vitro tests to predict tumors’ drug responsiveness. We have developed an in vitro short-term test based on nucleic acid precursor incorporation to determine clinical drug resistance. This test detects inherent and acquired resistance in vitro and transplantable syngeneic and xenografted tumors in vivo. In several clinical trials, clinical resistance was predictable with more than 90% accuracy, while drug sensitivity was detected with less accuracy (~60%). Remarkably, clinical cross-resistance to numerous drugs (multidrug resistance, broad spectrum resistance) was detectable by a single compound, doxorubicin, due to its multifactorial modes of action. The results of this predictive test were in good agreement with predictive assays of other authors. As no predictive test has been established as yet for clinical diagnostics, the identification of sensitive drugs may not reach sufficiently high reliability for clinical routine. A meta-analysis of the literature published during the past four decades considering test results of more than 15,000 tumor patients unambiguously demonstrated that, in the majority of studies, resistance was correctly predicted with an accuracy between 80 and 100%, while drug sensitivity could only be predicted with an accuracy of 50–80%. This synopsis of the published literature impressively illustrates that prediction of drug resistance could be validated. The determination of drug resistance was reliable independent of tumor type, test assay, and drug used in these in vitro tests. By contrast, chemosensitivity could not be predicted with high reliability. Therefore, we propose a rethinking of the “chemosensitivity” concept. Instead, predictive in vitro tests may reliably identify drug-resistant tumors. The clinical consequence imply to subject resistant tumors not to chemotherapy, but to other new treatment options, such as antibody therapy, adoptive immune therapy, hyperthermia, gene therapy, etc. The high accuracy to predict resistant tumors may be exploited to develop new strategies for individualized cancer therapy. This new concept bears the potential of a revival of predictive tests for personalized medicine.

Machine Learning for Prediction of HIV Drug Resistance: A Review

Machine Learning for Prediction of HIV Drug Resistance: A Review

Predicting drug resistance in adult patients with generalized epilepsy: A case-control study.

Using an adult cohort of patients with generalized epilepsy, we aimed to identify risk factors for development of drug-resistant epilepsy (DRE), which if identifiable would allow patients to receive earlier treatment and more specifically individualized treatment plans. For the case-control study, 118 patients with generalized epilepsy (GE) between the ages of 18 and 75 were included after selection from a database of 800 patients referred from throughout the Saskatchewan Epilepsy Program. Definitions were used in accordance with ILAE criteria. The odds ratio and its confidence interval were calculated. We performed a logistic regression analysis. Forty-four (37%) patients fulfilled the definition of DRE (cases), and seizures in 74 (63%) patients were not intractable (controls). Patients with DRE were significantly younger than the controls at the onset of epilepsy (6.6 vs. 18.8 years, p=<0.001). Significant variables on univariate analysis were the following: epilepsy diagnosed prior to 12 years (OR: 12.1, CI: 4.8-29.9, p<0.001), previous history of status epilepticus (OR: 15.1, CI: 3.2-70.9, p<0.001), developmental delay (OR: 12.6, CI: 4.9-32, p<0.001), and cryptogenic epilepsy (OR: 10.5, CI: 3.9-27.8, p<0.001). Our study showed some protective factors for DRE such as a good response to first AED, idiopathic etiology, and history of febrile seizures. In the logistic regression analysis, two variables remained statistically significant: developmental delay and more than one seizure type. Our study has identified a set of variables that predict DRE in patients with generalized epilepsy. Risk factors identified in our study are similar to those previously identified in pediatric studies, however, our study is specifically tailored to adult patients with generalized epilepsy.

Catamenial epilepsy as a predictor of drug resistance

Catamenial epilepsy as a predictor of drug resistance

A Collaborative Approach for “ReSeq-ing” Mycobacterium tuberculosis Drug Resistance: Convergence for Drug and Diagnostic Developers

A Collaborative Approach for “ReSeq-ing” Mycobacterium tuberculosis Drug Resistance: Convergence for Drug and Diagnostic Developers

Effect of Chemotherapeutic Drugs on Caspase-3 Activity, as a Key Biomarker for Apoptosis in Ovarian Tumor Cell Cultured as Monolayer. A Pilot Study.

We aimed to develop a cost-effective and robust method to predict drug resistance in individual patients. Representative tissue fragments were obtained from tumors removed from female patients, aged 24-74 years old. The tumor tissue was taken by a histopathology's or a surgeon under sterile conditions. Cells obtained by enzymatic dissociation from tumor after surgery, were cultured as a monolayer for 6 days. Paclitaxel, doxorubicin, carboplatin and endoxan alone or in combination were added at the beginning of culture and after 6 days, Alamar blue test was used for showing action on cell proliferation why caspase- 3 activity assays for verifying action on apoptosis. Inhibitory action on cell proliferation was noted in 2 of 12 patients tumor treated with both single and combined drugs. Using caspase-3 assay we showed that 50% of tumor cells was resistant to single chemotherapeutic drugs and 40% for combined. In 2 of 12 tumors, which did not reacted on single drugs, positive synergistic action on cell proliferation was observed in combination of D + E and C + E. This pilot study suggests: 1) monolayer culture of tumor cells, derived from individual patients, before chemotherapy could provide a suitable model for studying resistance for drugs; 2) caspase-3 activity is cheap and useful methods; 3) Alamar blue test should be taken into consideration for measuring cell proliferation.

Predicting drug resistance before it occurs

Koch and colleagues define possible resistance mechanisms to a small molecule inhibitor before it is tested in cancer patients.

Rapid prediction of inducible clarithromycin resistance in Mycobacterium abscessus

We have developed a single tube TaqMan® real-time PCR assay that differentiates the full-length and truncated erm(41) gene to predict inducible resistance to clarithromycin in Mycobacterium abscessus. A study of 87 clinical isolates found this assay to be 90.8% concordant to conventional drug susceptibility testing results for the prediction of inducible clarithromycin drug resistance.

Potential role of exosome-associated microRNA panels and in vivo environment to predict drug resistance for patients with multiple myeloma in the era of novel agents: A retrospective analysis of real-world data from a Chinese center of hematological disease

Potential role of exosome-associated microRNA panels and in vivo environment to predict drug resistance for patients with multiple myeloma in the era of novel agents: A retrospective analysis of real-world data from a Chinese center of hematological disease

HIV Drug Resistance Surveillance Among Jamaican Men Who Have Sex with Men Should Be Prioritized for Reducing HIV Transmission.

The prevalence of human immunodeficiency virus type 1 (HIV-1) is highest among men who have sex with men (MSM) in Jamaica but no genotypic data are available on the virus strains that are responsible for the epidemic among this key population. HIV-1 polymerase (pol) genes from 65 MSM were sequenced and used to predict drug resistance mutations. An HIV drug resistance prevalence of 28% (minimum 13%) was observed among this cohort, with the most frequent mutations conferring resistance to efavirenz, nevirapine, and lamivudine. Phylogenetic analysis of the sequences revealed 10 times the number of linked HIV infections among this cohort than respondent reporting. HIV treatment and prevention efforts in Jamaica could benefit significantly from Pol genotyping of the HIV strains infecting socially vulnerable MSM prior to initiating antiretroviral therapy (ART), as this would guide suppressive ART and unearth HIV transmission clusters to enable more effective delivery of treatment and prevention programs.

Abstract A109: Dual mTOR/PI3K inhibitors induce compensatory MEK/ERK activation in human pancreatic cancer cells: A mechanism contributing to drug resistance

Abstract Background: The phosphatidylinositol 3-kinase (PI3K)/Akt/mTORC1/S6K pathway is aberrantly activated in premalignant pancreatic lesions and in pancreatic ductal adenocarcinoma (PDAC) tissues. The pathway functions downstream of RAS (mutated in 90% of PDAC) and plays a key role in tyrosine kinase receptor signaling. Consequently, mTORC1 and the upstream components of the cascade are attractive therapeutic targets in PDAC. However, mTORC1 also mediates potent negative feedback loops that restrain upstream signaling via insulin/IGF receptor and other tyrosine kinase receptors, as shown with first and second generation of mTOR inhibitors. Dual PI3K and mTOR inhibitors (PI3K/TOR-KIs) have been developed to block completely mTORC1 and mTORC2 and prevent upstream pathway activation. Surprisingly, we report here that dual PI3K/TOR-KIs induce robust MEK/ERK over-activation in PDAC cells via a PI3K-independent mechanism. Results: Treatment of PANC-1 or MiaPaCa-2 pancreatic cancer cells with NPV-BEZ235 (0.005-1µM), a clinically relevant dual PI3K/TOR-KI, potently blocked mTORC1/S6K activation (scored by S6 phosphorylation at Ser240/244), mTORC1/4E-BP1 (assayed by 4E-BP1 phosphorylation at Thr37/46) and mTORC2-mediated Akt phosphorylation at Ser473, in a concentration-dependent manner. Strikingly, exposure to NPV-BEZ235 markedly enhanced the increase in the phosphorylation of ERK at Thr202 and Tyr204. Maximal ERK over-activation (~3 fold) coincided with complete inhibition of phosphorylation of Akt and 4E-BP1 (0.1-0.5 µM NPV-BEZ235). ERK over-activation was demonstrated when PDAC cells were stimulated with serum growth factors or insulin and neurotensin as well as when different PI3K/TOR-KIs (PKI-587, GDC-0980) were used instead of NPV-BEZ235. In order to prove that dual PI3K/TOR-KIs over-activate ERK through a novel PI3K-independent pathway in PDAC cells, we determined the effect of NEV-BEZ235, PKI-587 and GDC-0980 on PI3K-generated accumulation of PIP3 in the plasma membrane of single MiaPaCa-2 cells, using distribution of Akt-PH-GFP to monitor PIP3. Stimulation with insulin induced striking translocation of the PIP3 sensor to the plasma membrane, indicative of robust PI3K activation. Exposure to NEV-BEZ235, PKI-587 or GDC-0980 completely prevented the translocation of Akt-PH-GFP to the plasma membrane, providing conclusive evidence that dual PI3K/mTOR inhibitors induce rapid MEK/ERK activation in PDAC cells with suppressed PI3K activity. Additionally, we showed that treatment with the MEK inhibitors U126 or PD0325901 (1-5 µM) prevented ERK over-activation induced by PI3K/TOR-KIs. PD0325901 released feedback inhibition of RAS/RAF, as revealed by over-phosphorylation of MEK in MiaPaCa-2 or PANC-1 cells. Interestingly, NVP-BEZ235 induced further enhancement of MEK phosphorylation in PDAC cells treated with PD-0325901, implying that dual PI3K/mTOR inhibitor enhanced Raf/MEK in cells without ERK-mediated negative feedbacks loops. Finally, in order to examine whether the over-activation of the ERK pathway counterbalances the growth-suppressive effect of PI3K/TOR-KIs, PDAC cells were treated with PI3K/TOR-KIs (NPV-BEZ235 or PKI-587 or GDC-0980) and PD0325901 or a combination of either PI3K/TOR-KIs and PD0325901. The combination of these drugs caused a more pronounced inhibition of cell growth than that produced by each inhibitor added individually. Conclusion: Collectively, our results highlight the importance of discovering novel signaling crosstalk to anticipate mechanisms of tumor resistance to new drugs. The capability of predicting drug resistance can assist in developing rational and effective strategies for developing combination therapies in PDAC. Citation Format: Heloisa P. Soares, Ming Ming, James Sinnett-Smith, Michelle Mellon, Enrique Rozengurt. Dual mTOR/PI3K inhibitors induce compensatory MEK/ERK activation in human pancreatic cancer cells: A mechanism contributing to drug resistance. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Innovations in Research and Treatment; May 18-21, 2014; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2015;75(13 Suppl):Abstract nr A109.

Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study

SummaryBackgroundDiagnosing drug-resistance remains an obstacle to the elimination of tuberculosis. Phenotypic drug-susceptibility testing is slow and expensive, and commercial genotypic assays screen only common resistance-determining mutations. We used whole-genome sequencing to characterise common and rare mutations predicting drug resistance, or consistency with susceptibility, for all first-line and second-line drugs for tuberculosis.MethodsBetween Sept 1, 2010, and Dec 1, 2013, we sequenced a training set of 2099 Mycobacterium tuberculosis genomes. For 23 candidate genes identified from the drug-resistance scientific literature, we algorithmically characterised genetic mutations as not conferring resistance (benign), resistance determinants, or uncharacterised. We then assessed the ability of these characterisations to predict phenotypic drug-susceptibility testing for an independent validation set of 1552 genomes. We sought mutations under similar selection pressure to those characterised as resistance determinants outside candidate genes to account for residual phenotypic resistance.FindingsWe characterised 120 training-set mutations as resistance determining, and 772 as benign. With these mutations, we could predict 89·2% of the validation-set phenotypes with a mean 92·3% sensitivity (95% CI 90·7–93·7) and 98·4% specificity (98·1–98·7). 10·8% of validation-set phenotypes could not be predicted because uncharacterised mutations were present. With an in-silico comparison, characterised resistance determinants had higher sensitivity than the mutations from three line-probe assays (85·1% vs 81·6%). No additional resistance determinants were identified among mutations under selection pressure in non-candidate genes.InterpretationA broad catalogue of genetic mutations enable data from whole-genome sequencing to be used clinically to predict drug resistance, drug susceptibility, or to identify drug phenotypes that cannot yet be genetically predicted. This approach could be integrated into routine diagnostic workflows, phasing out phenotypic drug-susceptibility testing while reporting drug resistance early.FundingWellcome Trust, National Institute of Health Research, Medical Research Council, and the European Union.

Towards genomic prediction of drug resistance in tuberculosis

Towards genomic prediction of drug resistance in tuberculosis

Using Hierarchical Virtual Screening To Combat Drug Resistance of the HIV-1 Protease.

Human immunodeficiency virus (HIV) protease inhibitors (PIs) are important components of highly active anti-retroviral therapy (HAART) that block the catalytic site of HIV protease, thus preventing maturation of the HIV virion. However, with two decades of PI prescriptions in clinical practice, drug-resistant HIV mutants have now been found for all of the PI drugs. Therefore, the continuous development of new PI drugs is crucial both to combat the existing drug-resistant HIV strains and to provide treatments for future patients. Here we purpose an HIV PI drug design strategy to select candidate PIs with binding energy distributions dominated by interactions with conserved protease residues in both wild-type and various drug-resistant mutants. On the basis of this strategy, we have constructed a virtual screening pipeline including combinatorial library construction, combinatorial docking, MM/GBSA-based rescoring, and reranking on the basis of the binding energy distribution. We have tested our strategy on lopinavir by modifying its two functional groups. From an initial 751 689 candidate molecules, 18 candidate inhibitors were selected using the pipeline for experimental validation. IC50 measurements and drug resistance predictions successfully identified two ligands with both HIV protease inhibitor activity and an improved drug resistance profile on 2382 HIV mutants. This study provides a proof of concept for the integration of MM/GBSA energy analysis and drug resistance information at the stage of virtual screening and sheds light on future HIV drug design and the use of virtual screening to combat drug resistance.

WE-D-BRB-02: Predicting Outcomes of Primary and Metastatic Pancreatic Cancer with Principles of Mass Transport

Discoveries in cancer research are impacting patients through development of new therapeutics and better diagnostic tools. However, there is still a lack of understanding about heterogeneity in patient response to these therapeutics, optimizing therapeutic dosing schedules, or predicting complex metastatic patterns due to the complexity of the disease. Heterogeneity of patient response to treatment stems from the intricate interactions with the microenvironment, and distant organs and other environmental pressures including low oxygen, high acidity, and toxic drugs. To deconvolute some of this complexity physical scientists are using intricate tools to improve cancer diagnosis and predict drug resistance. Also to predict tumor growth and response to therapeutics, computational scientists are developing mathematical tools that link physical sciences or evolutionary sciences approaches with experimental and clinical data. These models yield predictions based on initial parameters obtained from patient clinical data, such as noninvasive imaging. This session will highlight the utilization of these mathematical and physical tools to predict patient response to treatment options, predict the optimal drug dosing schedule, and improve cancer diagnosis. This approach could be further enhanced by developing tools and models to chart the progress of stressed, aging tissue towards the progression to uncontrolled cell growth and mutational meltdown even before the diagnosis. Learning Objectives: 1. To learn about treatment response heterogeneity 2. To learn about tumor evolutionary capabilities 3. To learn about mathematical tools that link physical sciences with experimental and clinical data

WE-D-BRB-00: Modeling Cancer Complexity

Discoveries in cancer research are impacting patients through development of new therapeutics and better diagnostic tools. However, there is still a lack of understanding about heterogeneity in patient response to these therapeutics, optimizing therapeutic dosing schedules, or predicting complex metastatic patterns due to the complexity of the disease. Heterogeneity of patient response to treatment stems from the intricate interactions with the microenvironment, and distant organs and other environmental pressures including low oxygen, high acidity, and toxic drugs. To deconvolute some of this complexity physical scientists are using intricate tools to improve cancer diagnosis and predict drug resistance. Also to predict tumor growth and response to therapeutics, computational scientists are developing mathematical tools that link physical sciences or evolutionary sciences approaches with experimental and clinical data. These models yield predictions based on initial parameters obtained from patient clinical data, such as noninvasive imaging. This session will highlight the utilization of these mathematical and physical tools to predict patient response to treatment options, predict the optimal drug dosing schedule, and improve cancer diagnosis. This approach could be further enhanced by developing tools and models to chart the progress of stressed, aging tissue towards the progression to uncontrolled cell growth and mutational meltdown even before the diagnosis. Learning Objectives: 1. To learn about treatment response heterogeneity 2. To learn about tumor evolutionary capabilities 3. To learn about mathematical tools that link physical sciences with experimental and clinical data

WE-D-BRB-01: Fragile Proteins and the Origins of Cancer

Discoveries in cancer research are impacting patients through development of new therapeutics and better diagnostic tools. However, there is still a lack of understanding about heterogeneity in patient response to these therapeutics, optimizing therapeutic dosing schedules, or predicting complex metastatic patterns due to the complexity of the disease. Heterogeneity of patient response to treatment stems from the intricate interactions with the microenvironment, and distant organs and other environmental pressures including low oxygen, high acidity, and toxic drugs. To deconvolute some of this complexity physical scientists are using intricate tools to improve cancer diagnosis and predict drug resistance. Also to predict tumor growth and response to therapeutics, computational scientists are developing mathematical tools that link physical sciences or evolutionary sciences approaches with experimental and clinical data. These models yield predictions based on initial parameters obtained from patient clinical data, such as noninvasive imaging. This session will highlight the utilization of these mathematical and physical tools to predict patient response to treatment options, predict the optimal drug dosing schedule, and improve cancer diagnosis. This approach could be further enhanced by developing tools and models to chart the progress of stressed, aging tissue towards the progression to uncontrolled cell growth and mutational meltdown even before the diagnosis. Learning Objectives: 1. To learn about treatment response heterogeneity 2. To learn about tumor evolutionary capabilities 3. To learn about mathematical tools that link physical sciences with experimental and clinical data